Machine Learning Applications in Power System Condition Monitoring

Dear Colleagues,

I am inviting submissions for a Special Issue of Energies on the subject area of “Machine Learning Applications in Power System Condition Monitoring”. In recent years, power systems have undergone a once in a generation transformation to accommodate low carbon technologies while supporting ever higher expectations of service level. New technology and legacy plants are expected to co-exist seamlessly on networks that are being used outside of their original design specification through schemes such as dynamic rating. Condition monitoring offers a route to facilitating this but only if data can be reduced to an interpretable form, which is where machine learning offers leverage. Supporting existing domain expertise with higher resolution operational insight unlocks the investment in condition monitoring, and here the design of appropriate analytics and automation is key. Whether at generation, transmission, distribution, or end use, power assets are diverse and their performance is reflective of their health and operating environment. Accordingly, topics of interest for this Special Issue include, but are not limited to:

• Monitoring of renewable generation
• Monitoring of legacy assets
• Transmission and distribution network assets
• Prognostics for battery energy storage
• Minimal data availability
• Condition monitoring of power electronics
• Explicable machine learning
• Integration of machine learning with physics based models

Dr. Bruce Stephen
Guest Editor

Manuscript Submission Information

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